Contagion dynamics are ubiquitous in biological and social systems. They can encompass the spread of disease [1]}, [2]}, rumors [3]}, [4]}, [5]}, behaviors [6]}, [7]}, or information [8]}, [9]}.
These different types of phenomena are often modeled using generic descriptions that were developed to represent infection spreading and epidemic outbreaks, in which individuals can be in a susceptible, infectious, or recovered state.
In these models, the contagion typically propagates through an interaction network that plays a decisive role in the spreading dynamics.
In realistic scenarios, the interactions are time-dependent and determined by a proximity network that changes as a function of the positions of the individuals.
In cases where these agents follow complex collective dynamics, the changing topology can depend in turn on their self-organized motion.
To improve our modeling of real-world systems, it is therefore important to understand the relationship between the emergence of large-scale contagion processes and the evolving interaction networks that develop between agents that display collective behavior [10]}.
